[HN Gopher] WeatherNext 2: Our most advanced weather forecasting...
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WeatherNext 2: Our most advanced weather forecasting model
Author : meetpateltech
Score : 172 points
Date : 2025-11-17 15:04 UTC (7 hours ago)
(HTM) web link (blog.google)
(TXT) w3m dump (blog.google)
| xd1936 wrote:
| Pricing, I think?
|
| https://developers.google.com/maps/billing-and-pricing/prici...
| LogicFailsMe wrote:
| This year, the wild variance in hourly weather reports on my
| phone has really been something. I attributed it to likely budget
| cuts as a result of DOGE, but if those forecasts came from Google
| itself the whole time, all is clear now.
| politelemon wrote:
| I find that unlikely, my forecasts for much of Europe and East
| Asia have been consistently accurate.
| dylan604 wrote:
| How do DOGE implemented budget cuts affect European or East
| Asian forecasts? Those are not the forecasts that someone
| suspecting departmental DOGEing to be a fault.
| NoiseBert69 wrote:
| If the US does less data gathering (balloon starts, buoy
| maintenance, setting up weather huts in super remote sites,
| etc.) it will affect all forecasts.
|
| Models all use a "current world state" of all sensors
| available to bootstrap their runs.
|
| Similar thing happened during the beginning of Covid-19:
| they are using modified cargo/passenger planes to gather
| weather data during their routine trips. Suddenly this huge
| data source was gone (but was partially replaced by the
| experimental ADM-Aeolus satellite - which turned out to be
| a huge global gamer changer due to its unexpected high
| quality data)
| integralid wrote:
| But GP said they only USED TO blame DOGE, and blame Google
| now?
| LogicFailsMe wrote:
| Yeah... So you know that's not the United States right?
| Though judging by the down votes, it's quite triggering for
| some and I can't say which side when I pivot from blaming
| DOGE to blaming bad AI. Curious(tm)...
|
| And I say that as a huge fan of AI, but being vocally self-
| critical is an important attribute for professional success
| in AI and elsewhere.
| binsquare wrote:
| I find it interesting that they quantify the improvement on speed
| and number of forecast-ed scenarios but lack details on how it
| results in improved accuracy of the forecast per:
|
| ``` WeatherNext 2 can generate forecasts 8x faster and with
| resolution up to 1-hour. This breakthrough is enabled by a new
| model that can provide hundreds of possible scenarios. ```
|
| As an end user, all I care is that there's one accurate
| forecasted scenario.
| NoiseBert69 wrote:
| As a end user I also want to see the variance to get a feeling
| of the uncertainty.
|
| Quite a lot of weather sites offer this data in an easily
| eatable visual format.
| Sanzig wrote:
| Indeed. The most important benchmark is accuracy and how well
| it stacks up against existing physics-based models like GFS or
| ECMWF.
|
| Sure, those big physics-based models are very computationally
| intensive (national weather bureaus run them on sizeable HPC
| clusters), but you only need to run them every few hours in a
| central location and then distribute the outputs online. It's
| not like every forecaster in a country needs to run a model,
| they just need online access to the outputs. Even if they
| _could_ run the models themselves, they would still need the
| mountains of raw observation data that feeds the models
| (weather stations, satellite imagery, radars, wind
| profilers...). And these are usually distributed by... the
| national weather bureau of that country. So the weather bureau
| might as well do the number crunching as well and distribute
| that.
| agildehaus wrote:
| They integrated "MetNet-3" into Google products and my personal
| perception was accuracy decreased.
| tylervigen wrote:
| For lay-users they could have explained that better. I think
| they may not have completely uninformed users in mind for this
| page though.
|
| Developing an ensemble of possible scenarios has been the
| central insight of weather forecasting since the 1960s when
| Edward Lorenz discovered that tiny differences in initial
| conditions can grow exponentially (the "butterfly effect").
| Since they could really do it in the 90s, all competitive
| forecasts are based on these ensemble models.
|
| When you hear "a 70% chance of rain," it more or less means
| "there was rain in 70 of the 100 scenarios we ran."[0] There is
| no "single accurate forecast scenario."
|
| [0] Acknowledging this dramatically oversimplifies the models
| and the location where the rain could occur.
| sweettea wrote:
| My understanding is that it's an expected value based on
| coverage in each of the ensemble scenarios, not quite as
| simplified as "how many scenarios was there rain in this
| forecast cell".
|
| At least for the US NWS: if 30 of 100 scenarios result in 50%
| shower coverage, and 70 out of 100 result in 0%, this is
| reported as 15% chance of rain. Which is exactly the same as
| 15 with 100% coverage and 85 with 0% coverage, or 100 with
| 15% coverage.
|
| Understanding this, and digging further into the forecast,
| gives a better sense of whether you're likely to encounter
| widespread rainfall or spotty rainfall in your local area.
| meandthewallaby wrote:
| This is really important: You're not the end user of this
| product. These types of models are not built for laypeople to
| access them. You're an end user of a product that may use and
| process this data, but the CRPS scorecard, for example, should
| mean nothing to you. This is specifically addressing an under-
| dispersion problem in traditional ensemble models, due to a
| limited number (~50) and limited set of perturbed initial
| conditions (and the fact that those perturbations do very
| poorly at capturing true uncertainty).
|
| Again, you, as an end user, don't need to know any of that. The
| CRPS scorecard is a very specific measure of error. I don't
| expect them to reveal the technical details of the model, but
| an industry expert instantly knows what WeatherBench[1] is, the
| code it runs, the data it uses, and how that CRPS scorecard was
| generated.
|
| By having better dispersed ensemble forecasts, we can more
| quickly address observation gaps that may be needed to better
| solidify certain patterns or outcomes, which will lead to more
| accurate deterministic forecasts (aka the ones you get on your
| phone). These are a piece of the puzzle, though, and not one
| that you will ever actually encounter as a layperson.
|
| [1]: https://sites.research.google/gr/weatherbench/
| counters wrote:
| > By having better dispersed ensemble forecasts, we can more
| quickly address observation gaps that may be needed to better
| solidify certain patterns or outcomes, which will lead to
| more accurate deterministic forecasts.
|
| Sorry - not sure this is a reasonable take-away. The models
| here are all still initialized from analysis performed by
| ECMWF; Google is not running an in-house data assimilation
| product for this. So there's no feedback mechanism between
| ensemble spread/uncertainty and the observation itself in
| this stack. The output of this system could be interrogated
| using something like Ensemble Sensitivity Analysis, but
| there's nothing novel about that and we can do that with
| existing ensemble forecast systems.
| DoctorOetker wrote:
| Sorry to hijack you: I have some questions regarding current
| weather models:
|
| I am personally _not_ interested in predicting the weather as
| end users expect it, rather I am interested in representative
| evolutions of wind patterns. I.e. specify some location (say
| somewhere in the North Sea, or perhaps on mainland Western
| Europe), and a date (say Nov 12) without specifying a year,
| and would like to have the wind patterns at different heights
| for that location say for half an hour. Basically running
| with different seeds, I want to have representative
| evolutions of the wind vector field (without specifying
| starting conditions, other than location and date, i.e. NO
| prior weather).
|
| Are there any ML models capable of delivering realistic and
| representative wind gust models?
|
| (The context is structural stability analysis of hypothetical
| megastructures)
| sails wrote:
| As others have explained, ensembles are useful.
|
| As a layperson, what _is_ useful is to look at the difference
| between models. My long range favourite is to compare ECMWF and
| GFS27 and if the deviation is high (windy app has this) then
| you can bet that at least one of them is likely wrong
| jakewins wrote:
| Is anyone aware of good sources of higher resolution models?
| Hourly resolution like this model provides doesn't help much now
| that energy markets have moved to 15-min and 5-min resolution.
| vaughnegut wrote:
| Windy allows you to select your model. For that reason it's my
| go to for accuracy.
|
| Different models have different strengths, though. Some are
| shorter range (72h) or longer range (1-3 weeks). Some are
| higher resolution for where you live (the size of an area which
| it assigns a forecast to, so your forecast is more local).
|
| Some governments will have their own weather model for your
| country that is the most accurate for where you live. What I
| did for a long time was use Windy and use HDRPS (a Canadian
| short range model with a higher resolution in Canada so I have
| more accurate forecasts). Now I just use the government of
| Canada weather app.
|
| I genuinely wonder what the weather Channel, iPhone/Android
| official weather apps, etc. use under the hood for global
| models. My gut says ECMWF (a European model with global
| coverage) mixed with a little magic.
| chneu wrote:
| Windy or Ventusky. Both really solid.
| mlmonkey wrote:
| How does one use weather data in an energy market, if you don't
| mind my asking?
| bitdivision wrote:
| Seems like it would be pretty useful to forecast the supply
| of renewables (wind, solar, maybe some hydro).
| edbaskerville wrote:
| Indeed. In the not-too-distant future where renewables are
| the vast majority of generation (sooner in China than in
| the U.S. at current rates of progress), the weather matters
| more and more.
| hackitup7 wrote:
| Temperature and weather can have a huge impact on power
| prices. Small examples:
|
| * 90 degree day => more air conditioning usage => power goes
| up
|
| * 70 degree sunny day => that's also July 4th (holiday, not a
| work day when factories or heavy industry are running) =>
| lots of people go outside + it's a holiday => power
| consumption goes DOWN
|
| * 10 degree difference colder/hotter => impacts resistance of
| power lines => impacts transmission congestion credits =>
| impacts power prices
|
| It's a fascinating industry. One power trading company that I
| consulted for had a meteorologist who was also a trader. They
| literally hired the dude from a news channel if I remember it
| correctly.
| jakewins wrote:
| Yeah exactly like hackitup7 says, it has a huge impact on
| both sides of the supply and demand equation. It both drives
| house heating and cooling, which has a massive consumption
| impact, and it drives solar and wind production.
|
| But knowing "there will be a massive drop in temperature
| between 1pm->2pm" doesn't help much anymore, you need to know
| which 15-minute or 5-minute block all those heat pumps will
| kick on in, to align with markets moving to 15-min and 5-min
| contracts.
|
| Major forecasts like ECMWF don't have anything like that
| resolution; they model the planet at 3 hour time scale, with
| a 1 hour "reanalysis" model called ERA5.. hoping to find good
| info on what's available at higher resolution.
| trillic wrote:
| HRRR is 15 min res updated hourly. It's not that resolution all
| the way out only 18 hours I think.
| timenotwasted wrote:
| It feels like real weather
| AI|Forecast|whatever_you_want_to_call_it is still far, far away.
| Maybe it's just the consumer aspect of weather apps but I don't
| feel as if I get any more accurate data now than I did back when
| my parents turned to the daily weather channel for the forecast.
| Still a lot of clear days when rain was predicted or the even
| more dreaded torrential downpour when it was supposed to be sunny
| and clear.
|
| Obviously all I have is anecdata for what I'm mentioning here but
| from a consumer perspective I don't feel like these model
| enhancements are really making average folks feel as if weather
| is any more understood than it was decades ago.
| jstummbillig wrote:
| No need for anecdata! We have the data:
| https://ourworldindata.org/weather-forecasts
|
| tdlr: Weather forecasts have improved a lot
| timenotwasted wrote:
| That's actually really helpful to understand better, thank
| you!
| baron816 wrote:
| I remember when it was a trope that the weatherman was always
| wrong and that the weather was the prototypal thing that was
| inherently "unpredictable".
| deanputney wrote:
| I've found this to be more related to poor representation of
| the data than inaccurate data.
|
| For example on Apple's Weather app, a "rainy" day means a high
| chance of rain at any point during the day. If it's 80% chance
| of rain at 5am and sunny the rest of the day- that counts as
| rainy. You can see an hourly report for more info, and
| generally this is pretty accurate. You have to learn how to
| find the right data, know your local area, and interpret it
| yourself.
|
| Then you have to consider what effects this has on your plans
| and it gets more complicated. Finding a window to walk the dog,
| choosing a day to go sailing, or determining conditions for
| backcountry skiing all have different requirements and
| resources. What I'd like AI to do is know my own interests and
| highlight what the forecast means for me.
| semi-extrinsic wrote:
| In Norway people are extremely weather-focused, and the
| national weather service delivers quite advanced graphics for
| people to understand what is going on.
|
| The standard graph that most people look at to get an idea
| about today and tomorrow: https://www.yr.no/en/forecast/graph
| /1-72837/Norway/Oslo/Oslo...
|
| The live weather radar which shows where it is raining right
| now and prediction/history for rain +/- 90 minutes. This is
| accurate enough that you can use it to time your walk from
| the office to the subway and avoid getting wet:
| https://www.yr.no/en/map/radar/1-72837/Norway/Oslo/Oslo/Oslo
|
| Then you have more specialised forecasts of course. Dew
| point, feels like temperature, UV, pollution, avalanche
| risks, statistics, sea conditions, tides, ... People tend to
| geek out quite heavily on these.
| Alexsky2 wrote:
| The United States (National Weather Service) has these too:
| https://www.weather.gov/forecastmaps/
|
| I use these and Windy: https://www.windy.com/
|
| In my experience, these forecasts are really good 5-7 days
| out, and then degrade in reliability (as you would expect
| from predictions of chaotic systems). The apps that show
| you a rain cloud and a percentage number are always
| terrible in my experience for some reason, even if the
| origin of the data is the same. I'm not sure why that might
| be.
| tylervigen wrote:
| > I don't feel as if I get any more accurate data now than I
| did back when my parents turned to the daily weather channel
| for the forecast.
|
| The accuracy improvement is provable. A four-day forecast today
| is as accurate as a one-day forecast 30 years ago. And this is
| supremely impressive, because the difficulty of predicting the
| weather grows exponentially, not linearly, with time.
|
| You are welcome to your feelings - and to be fair, I'm not sure
| that our understanding of the weather has improved as much as
| our computational power to extend predictions has.
| counters wrote:
| You're 100% correct, but there's a subtlety in what the
| commenter is talking about.
|
| Yes, _in aggregate_, forecasts are objectively, quantifiably
| better in 2025 than they were in 2005 let alone 1985. But any
| given, specific forecast may have unique and egregious
| failure modes. Look no further than the GFS' complete
| inability to lock on to the forecast track for Hurricane
| Melissa a month ago. This is dramatically compounded when you
| look at mesoscale forecast, where higher spatial resolution
| is a liability that leads to double-penalty errors (e.g.
| setting up a mesoscale snow squall band just slightly south
| of where it actually develops).
|
| And keep in mind that the benchmarks shared from this model
| product are evaluating an ensemble mean, which further
| confounds things. Even if the ensemble mean is well-
| calibrated and accurate, there can be critical spread from
| the ensemble members themselves.
| lysecret wrote:
| The thing is that regular weather forecasts are also not that
| great.
| atonse wrote:
| Anyone know whether we can use this to simulate hurricanes/floods
| in particular areas, instead of looking at real existing data and
| helping model an existing hurricane as it's happening? (which is
| definitely more important and impactful, but the simulation angle
| is the one I happen to be curious about at the moment).
|
| Like if I wanted to simulate whether something like Hurricane
| Melissa would've gone through a handful of southern US states,
| what would the effect have been, from an insurance or resiliency
| standpoint.
| counters wrote:
| That's not really what a weather model "does."
| cryptoz wrote:
| 15 years later and still no word from Google if they will use the
| barometers in Android devices to assimilate surface pressure
| data. It has been shown that this can improve forecast accuracy.
| I think IBM may be doing it with their weather apps, but
| Google/Apple would have dramatically more data available.
|
| Apple even bought Dark Sky, which purported to do this but never
| released any information - so I doubt they really did do it. And
| if they did, I doubt Apple continued the practice.
|
| Been waiting a long time to hear Google announce they'll use your
| barometer to give you a better forecast. Still waiting I guess.
| Majromax wrote:
| > 15 years later and still no word from Google if they will use
| the barometers in Android devices to assimilate surface
| pressure data.
|
| For WeatherNext, the answer is 'no'. The paper
| (https://arxiv.org/abs/2506.10772) describes in detail what
| data the model uses, and direct assimilation of user barometric
| data is not on the list.
| counters wrote:
| The community has mostly abandoned SPO data. It's
| extraordinarily difficult to use this data because of social
| issues like PII and technical ones like QA/QC. But even more
| importantly, there's very little compelling evidence that the
| data makes much of any difference whatsoever in real forecasts.
| AceJohnny2 wrote:
| Is this the same model as provided the most accurate hurricane
| predictions this season?
|
| https://arstechnica.com/science/2025/11/googles-new-weather-...
| lysecret wrote:
| Im pretty deep into this topic and what might be interesting to
| an outsider is that the leading models like neuralgcm/weathernext
| 1 before as well as this model now are all trained with a "crps"
| objective which I haven't seen at all outside of ml weather
| prediction.
|
| Essentially you add random noise to the inputs and train by
| minimizing the regular loss (like l1) and at the same time
| maximizing the difference between 2 members with different random
| noise initialisations. I wonder if this will be applied to more
| traditional genai at some point.
| rytill wrote:
| What is the goal of doing that vs using L2 loss?
| lysecret wrote:
| To encourage diversity between the different members in an
| ensemble. I think people are doing very similar things for
| MOE networks but im not that deep into that topic.
| sunshinesnacks wrote:
| The goal of using CRPS is to produce an ensemble that is a
| good probabilistic forecast without needing calibration/post
| processing.
|
| [edit: "without", not "with"]
| cleak wrote:
| That's pretty neat. It reminds me of how VAEs work:
| https://en.wikipedia.org/wiki/Variational_autoencoder
| nerdponx wrote:
| > Essentially you add random noise to the inputs and train by
| minimizing the regular loss (like l1) and at the same time
| maximizing the difference between 2 members with different
| random noise initialisations. I wonder if this will be applied
| to more traditional genai at some point.
|
| We recently had a situation where we specifically wanted to
| generate 2 "different" outputs from an optimization task and
| struggled to come up with a good heuristic for doing so. Not at
| all a GenAI task, but this technique probably would have helped
| us.
| bilsbie wrote:
| Where can I use this? I've been trying to find hyperlocal
| forecasts like darksky used to be.
| paxys wrote:
| > We're now taking our research out of the lab and putting it
| into the hands of users. WeatherNext 2's forecast data is now
| available in Earth Engine and BigQuery. We're also launching an
| early access program on Google Cloud's Vertex AI platform for
| custom model inference.
|
| > By incorporating WeatherNext technology, we've now upgraded
| weather forecasts in Search, Gemini, Pixel Weather and Google
| Maps Platform's Weather API. In the coming weeks, it will also
| help power weather information in Google Maps.
| jadbox wrote:
| Google Maps has... weather predictions?
| batrat wrote:
| if you search for a city usually it shows the current
| weather, but I've seen in some cities there is also a 7 day
| forecast
| abdullahkhalids wrote:
| If you want to accurately predict times for future trips,
| you need weather predictions.
| jachee wrote:
| Apple integrated the hyperlocal darksky stuff into their native
| Weather app. It had a few growing pains, but it's as good as it
| ever was, imho.
| baxtr wrote:
| Agreed.
|
| The one thing I'd like them to improve are the precipitation
| maps though. They just feel awkward and unreliable.
| trillic wrote:
| The HRRR is VERY good in my opinion. It updates hourly with a
| 15-minute resolution 18 hours out and hourly 48 hours out.
|
| https://rapidrefresh.noaa.gov/hrrr/
| ellisv wrote:
| Precip.ai or go grab the MRMS data yourself
| SoftTalker wrote:
| Look out the window? Works as well as anything else for me.
| driverdan wrote:
| They link to the API: https://mapsplatform.google.com/maps-
| products/weather/
| carabiner wrote:
| I never understood the acclaim for dark sky. It never seemed
| very accurate, and the forecasts changed so rapidly that they
| weren't of much use. "Rain for next 2 hours" would become
| "Intermittent rain for the next 30 minutes" 10 minutes later.
| plantain wrote:
| Darksky was only ever good marketing.
| carabiner wrote:
| The UX was great but predictions were terrible. I swear the
| only people who liked it did so out of confirmation bias,
| which can affect anyone. Just a week ago here on HN, there
| were users here claiming Farmer's Almanac was accurate.
| lampiaio wrote:
| Reminds me of a funny WWII story:
|
| Kenneth Arrow and his statisticians found that their long-range
| forecasts were no better than numbers pulled out of a hat. The
| forecasters agreed and asked their superiors to be relieved of
| this duty. The reply was: "The Commanding General is well aware
| that the forecasts are no good. However he needs them for
| planning purposes."
| empath75 wrote:
| There is a fairly compelling argument that divination in the
| ancient world was not a useless waste of time, as is commonly
| assumed, but that having either a process or a person that can
| make essentially random choices for them allowed people to make
| hard, consequential decisions where they might otherwise be
| paralyzed, especially when the penalty for not acting was worse
| than making a mistake.
| twoodfin wrote:
| Fascinating. I suppose it also encourages developing
| adaptable strategies that accommodate imperfect information,
| vs. succumbing to wishful thinking or other forms of
| cognitive bias.
| OisinMoran wrote:
| I've also read that a source of randomness like that could
| help prevent things like over-extracting some land
| bigtones wrote:
| Googles weather prediction engine is already very good, and the
| new hurricane model was breathtakingly good this season when
| tested against actual hurricane paths. Meanwhile, the US
| Government Global Forecasting System continues to get worse.
|
| https://arstechnica.com/science/2025/11/googles-new-weather-...
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(page generated 2025-11-17 23:00 UTC)